We propose an approach to embed time series data in a vector space based on the distances obtained from Dynamic Time Warping (DTW), and to classify them in the embedded space. Under the problem setting in which both l...
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ISBN:
(纸本)3540269231
We propose an approach to embed time series data in a vector space based on the distances obtained from Dynamic Time Warping (DTW), and to classify them in the embedded space. Under the problem setting in which both labeled data and unlabeled data are given beforehand, we consider three embeddings, embedding in a Euclidean space by MDS, embedding in a Pseudo-Euclidean space, and embedding in a Euclidean space by the Laplacian eigenmap technique. We have found through analysis and experiment that the embedding by the Laplacian eigemnap method leads to the best classification result. Furthermore, the proposed approach with Laplacian eigenmap embedding shows better performance than k-nearest neighbor method.
During the last years, computer vision tasks like object recognition and localization were rapidly expanded from passive solution approaches to active ones, that is to execute a viewpoint selection algorithm in order ...
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ISBN:
(纸本)3540269231
During the last years, computer vision tasks like object recognition and localization were rapidly expanded from passive solution approaches to active ones, that is to execute a viewpoint selection algorithm in order to acquire just the most significant views of an arbitrary object. Although fusion of multiple views can already be done reliably, planning is still limited to gathering the next best view, normally the one providing the highest immediate gain in information. In this paper, we show how to perform a generally more intelligent, long-run optimized sequence of actions by linking them with costs. therefore it will be introduced how to acquire the cost of an appropriate dimensionality in a non-empirical way while still leaving the determination of the system's basic behavior to the user. Since this planning process is accomplished by an underlying machinelearning technique, we also point out the ease of adjusting these to the expanded task and show why to use a multi-step approach for doing so.
Concept lattice, core structure in Formal Concept Analysis has been used in various fields like software engineering and knowledge discovery. In this paper, we present the integration of Association rules and Classifi...
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ISBN:
(纸本)3540269231
Concept lattice, core structure in Formal Concept Analysis has been used in various fields like software engineering and knowledge discovery. In this paper, we present the integration of Association rules and Classification rules using Concept Lattice. this gives more accurate classifiers for Classification. the algorithm used is incremental in nature. Any increase in the number of classes, attributes or transactions does not require the access to the previous database. the incremental behavior is very useful in finding classification rules for real time data such as image processing. the algorithm requires just one database pass through the entire database. Individual classes can have different support threshold and pruning conditions such as criteria for noise and number of conditions in the classifier.
Whereas the early frequent patternmining methods admitted only relatively simple data and pattern formats (e.g., sets, sequences, etc.), there is nowadays a clear push towards the integration of ever larger portions ...
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ISBN:
(纸本)0769524958
Whereas the early frequent patternmining methods admitted only relatively simple data and pattern formats (e.g., sets, sequences, etc.), there is nowadays a clear push towards the integration of ever larger portions of domain knowledge in, the mining process in order to increase the precision and. the abstraction-level of the retrieved patterns and hence ease their interpretation. We present here a practically-motivated study of a frequent pattern extraction from sequences of data objects that are described within a domain ontology. As the complexity of the descriptive structures is high, an entire framework for the pattern extraction process had, to be defined. the key elements thereof are a pair of descriptive languages, one for individuals data and another one for generic patterns, a generality relation between patterns, and an Apriori-like method for patternmining.
In supervised machinelearning, the partitioning of the values (also called grouping) of a categorical attribute aims at constructing a new synthetic attribute which keeps the information of the initial attribute and ...
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ISBN:
(纸本)3540269231
In supervised machinelearning, the partitioning of the values (also called grouping) of a categorical attribute aims at constructing a new synthetic attribute which keeps the information of the initial attribute and reduces the number of its values. In case of very large number of values, the risk of overfilling the data increases sharply and building good groupings becomes difficult. In this paper, we propose two new grouping methods founded on a Bayesian approach, leading to Bayes optimal groupings. the first method exploits a standard schema for grouping models and the second one extends this schema by managing a "garbage" group dedicated to the least frequent values. Extensive comparative experiments demonstrate that the new grouping methods build high quality groupings in terms of predictive quality, robustness and small number of groups.
Ranked transformations should preserve a priori given ranked relations (order) between some feature vectors. Designing ranked models includes feature selection tasks. Components of feature vectors which are not import...
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ISBN:
(纸本)3540269231
Ranked transformations should preserve a priori given ranked relations (order) between some feature vectors. Designing ranked models includes feature selection tasks. Components of feature vectors which are not important for preserving the vectors order should be neglected. this way unimportant dimensions are greatly reduced in the feature space. It is particularly important in the case of "long" feature vectors, when a relatively small number of objects is represented in a high dimensional feature space, in the paper, we describe designing ranked models withthe feature selection which is based on the minimisation of convex and piecewise linear (CPL) functions.
We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method combines these primitives into high-le...
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ISBN:
(纸本)3540269231
We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method combines these primitives into high-level abstractions. Our appearance-based learning method uses local statistical analysis between features and Expectation-Maximization to identify and code spatial correlations. Spatial correlation is asserted when two features tend to occur at the same relative position of each other. this learning scheme results in a graphical model that constitutes a probabilistic representation of a flexible visual feature hierarchy. For feature detection, evidence is propagated using Belief Propagation. Each message is represented by a Gaussian mixture where each component represents a possible location of the feature. In experiments, the proposed approach demonstrates efficient learning and robust detection of object models in the presence of clutter and occlusion and under view point changes.
An integrated approach of mining association rules and meta-rules based on a hyper-structure is put forward. In this approach, time serial databases are partitioned according to time segments, and the total number of ...
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ISBN:
(纸本)3540269231
An integrated approach of mining association rules and meta-rules based on a hyper-structure is put forward. In this approach, time serial databases are partitioned according to time segments, and the total number of scanning database is only twice. In the first time, a set of 1-frequent itemsets and its projection database are formed at every partition. then every projected database is scanned to construct a hyper-structure. through miningthe hyper-structure, various rules, for example, global association rules, meta-rules, stable association rules and trend rules etc. can be obtained. Compared with existing algorithms for mining association rule, our approach can mine and obtain more useful rules. Compared with existing algorithms for meta-mining or change mining, our approach has higher efficiency. the experimental results show that our approach is very promising.
the scientific community has accumulated an immense experience in processing data represented in finite-dimensional linear spaces of numerical features of entities, whereas the kit of mathematical instruments for diss...
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ISBN:
(纸本)3540269231
the scientific community has accumulated an immense experience in processing data represented in finite-dimensional linear spaces of numerical features of entities, whereas the kit of mathematical instruments for dissimilarity-based processing of data in metric spaces representing distances between entities, for which sufficiently informative features cannot be found, is much poorer. In this work, the problem of embedding the given set of entities into a linear space with inner product by choosing an appropriate kernel function is considered as the major challenge in the featureless approach to estimating dependences in data sets of arbitrary kind. As a rule, several kernels may be heuristically suggested within the bounds of the same data analysis problem. We treat several kernels on a set of entities as Cartesian product of the respective number of linear spaces, each supplied with a specific kernel function as a specific inner product. the main requirement here is. to avoid discrete selection in eliminating redundant kernels withthe purpose of achieving acceptable computational complexity of the fusion algorithm.
As a powerful tool for summarizing the distributed medical information, Meta-analysis has played an important role in medical research in the past decades. In this paper, a more general statistical model for meta-anal...
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ISBN:
(纸本)3540269231
As a powerful tool for summarizing the distributed medical information, Meta-analysis has played an important role in medical research in the past decades. In this paper, a more general statistical model for meta-analysis is proposed to integrate heterogeneous medical researches efficiently. the novel model, named mixture random effect model (MREM), is constructed by Gaussian Mixture Model (GMM) and unifies the existing fixed effect model and random effect model. the parameters of the proposed model are estimated by Markov Chain Monte Carlo (MCMC) method. Not only can MREM discover underlying structure and intrinsic heterogeneity of meta datasets, but also can imply reasonable subgroup division. these merits embody the significance of our methods for heterogeneity assessment. Both simulation results and experiments on real medical datasets demonstrate the performance of the proposed model.
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